WO2022086112A1 - Method for analyzing ultrasound data obtained by passing through multiple layers - Google Patents

Abstract

The present invention relates to a method for analyzing ultrasound data obtained by passing through multiple layers, comprising the steps of: reducing noise in the ultrasound data; identifying an interface between layers by identifying the peak of the ultrasound data after removal of the noise; and differentiating each layer by identifying an attenuation coefficient of the ultrasound data after removal of the noise.

Description

How to analyze ultrasound data obtained by passing through multiple layers

The present invention relates to a method for analyzing ultrasound data obtained through a plurality of layers.

In the investigation of health conditions, it is important to accurately grasp the composition of the body. Changes in body composition (composition) are related to many diseases, and the occurrence of diseases or health status can be identified through changes in body composition.

There is a method using ultrasound to measure the thickness of muscle, fat, and bone among the composition of the body. Ultrasound has the advantage of being easy to test and convenient to use.

However, the existing method using ultrasound has a problem in that it is difficult to distinguish between layers due to other reflected data such as fat in the muscles.

Accordingly, it is an object of the present invention to provide a method for analyzing ultrasound data obtained through a plurality of layers.

An object of the present invention is to provide a method for analyzing ultrasound data obtained by passing through a plurality of layers, the method comprising: reducing noise of the ultrasound data; identifying an interface between layers by identifying a peak of the ultrasound data after the noise is removed; It is achieved by including the step of identifying each layer by identifying an attenuation coefficient of the ultrasound data after the noise is removed.

The reducing of the noise may include: removing in-band noise; out-of-band noise cancellation step; preserving peak positions in the band pass filtered ultrasound data; and converting the ultrasound data into amplitude data.

The peak position preservation may be performed by a zero-phase filtering method, and the amplitude data transformation may be performed by a Hilbert transform.

The step of recognizing the interface between the layers includes obtaining a local maximum value, and the local maximum value can be obtained using a minimum peak distance (MPD) and a minimum peak prominence (MPP).

After determining the interface between the layers, the method may further include determining the thickness of each layer by using the ultrasonic movement speed between the interfaces.

The plurality of layers may include at least two of bone, muscle, and fat.

The step of identifying each layer may further include correcting the diffraction characteristics of the transducer using ultrasound data obtained from a phantom for which the attenuation coefficient is known.

According to the present invention, there is provided a method of analyzing ultrasound data obtained through a plurality of layers.

1 is a flowchart illustrating a method for analyzing ultrasound data according to an embodiment of the present invention;

2a to 2c show the preparation process of the sample in the experimental example of the present invention,

Figure 3 shows the analysis process of ultrasound data in the experimental example of the present invention,

4a to 4e show step-by-step analysis of ultrasound data and MR images in an experimental example of the present invention;

5A and 5B are graphs showing the correlation between the thickness obtained by the analysis of ultrasound data and the thickness obtained by the MR image in the experimental example of the present invention.

Hereinafter, the present invention will be described in more detail with reference to the drawings.

Since the accompanying drawings are merely examples shown in order to explain the technical idea of the present invention in more detail, the spirit of the present invention is not limited to the accompanying drawings. In the accompanying drawings, the thickness or length of each part may be exaggerated for explanation.

The above-described embodiments are examples for explaining the present invention, and the present invention is not limited thereto. Those of ordinary skill in the art to which the present invention pertains will be able to practice the present invention with various modifications therefrom, so the technical protection scope of the present invention should be defined by the appended claims.

The following description exemplifies and describes a method for identifying and identifying animal or human tissue, particularly muscle/fat/bone, by analyzing ultrasound data, but the present invention is not limited thereto. The analysis method of the present invention may be used to analyze ultrasound data obtained through a plurality of various layers.

An ultrasound data analysis method according to the present invention will be described with reference to FIG. 1 . 1 is a flowchart illustrating a method of analyzing ultrasound data according to an embodiment of the present invention.

Ultrasound data to which the present invention is applied may be obtained by, for example, contacting an ultrasonic probe (probe) with a body such as an arm or a leg.

First, noise of ultrasound data is reduced (S110).

Noise reduction includes, but is not limited to, (1) in-band noise cancellation, (2) out-of-band noise cancellation, (3) preserving peak positions in bandpass filtered ultrasound data, and (4) ultrasound data may include the step of converting to amplitude data. The order of these steps may be varied.

Each of these steps may be performed using a known method, for example, peak position preservation may be performed by a zero-phase filtering method, and amplitude data transformation may be performed by a Hilbert transform.

Next, after noise removal, the peak of the ultrasound data is identified to determine the interface between the layers ( S120 ).

This step includes finding a local maximum. The local maximum value is not limited thereto, but may be obtained using a minimum peak distance (MPD) and a minimum peak prominence (MPP).

Next, each layer is identified by identifying an attenuation coefficient of the ultrasound data after noise is removed ( S130 ).

The attenuation coefficient is not limited thereto, but can be obtained by the following method.

First, ultrasound data in a time domain is changed into a frequency domain. Then, the centroid is calculated at each tissue depth in the frequency domain. Next, a change in the central point according to the tissue depth, that is, a frequency shift, is calculated. Finally, the attenuation coefficient is obtained using linear regression for the change of the center point according to the depth in the region of interest (ROI).

Each layer has its own range of attenuation coefficients, and each layer is identified by comparing it with the obtained attenuation coefficient.

In this step, the diffraction characteristics of the transducer can be corrected using the ultrasonic data obtained from the phantom with known attenuation coefficient.

Finally, the thickness of each layer is determined (S140). The thickness of each layer can be obtained by using the ultrasonic movement speed between the identified interfaces.

In the present invention, the order after noise removal may be changed. For example, the interface can be identified after obtaining the attenuation coefficient, and the attenuation coefficient can be obtained after determining the thickness.

According to the present invention, the identification and thickness of each layer can be determined, and the identification of a true peak using the local maximum value of the ultrasound data and identification of each layer is performed at each interface except for other reflection data such as intramuscular fat. to determine the thickness. Also, it is possible to simultaneously measure fat and muscle mass from ultrasound data.

Hereinafter, the present invention will be described in detail through experimental examples.

Preparation of measurement target

2a, 2b and 2c sequentially show the preparation of the measurement target. Three pig forelimbs were purchased from a butcher's shop, and 16 samples were made by amputation. Each sample was made to have a certain size, and each sample was made to include subcutaneous fat, muscle, and bone. The sample was marked with black ink where the ultrasonic measurement was to be performed.

Ultrasound data acquisition

Ultrasound data were obtained in areas marked with black ink for 16 samples.

A 2.25-MHz single-element focused transducer (BioSono Inc., USA) was excited with an ultrasonic pulser/receiver (Model XTR-2020, MKC Inc., Korea), and the obtained ultrasonic data was analyzed with an oscilloscope (at a 20-MS/s sampling rate). It was digitized using Model DSO1012A, Keysight Technologies, Korea) and stored in a computer. Each location was scanned 5 times, and the data were stored separately.

Ultrasound data analysis

Analysis of ultrasound data was performed using MATLAB (Mathworks, Natick, USA), and details will be described with reference to FIG. 3 .

3 is a schematic diagram of data processing performed in the present experimental example.

Step 1

Segment averaging was performed to reduce in-band noise for the M-segment.

[Correction 23.11.2021 under Rule 91]

where S(n) is the averaged ultrasound data (signal), s _m (n) is the mth ultrasound (RF) segment, M is the number of pulses in each measurement, and N is the number of digitized pulses during the pulse repetition interval. is the number After segment averaging, out-of-band noise was suppressed using a 5th-order Butterworth bandpass filter with cutoff frequencies of 500 kHz and 5 MHz, and zero-phase filtering was used to preserve the peak positions in the ultrasound data. Finally, Hilbert transform and log compression were performed to obtain log-compressed envelope signals.

step 2

Tissue interfaces are identified by finding local maxima within the log-compressed amplitude signal. The local maximum algorithm requires a minimum peak distance (MPD) and a minimum peak prominence (MPP). MPD determines the minimum separation between detected peaks; Therefore, the MPP must be greater than the peak duration. The prominence of a peak indicates its intrinsic height compared to other peaks. Using MPD of 1 μm and MPP of 10 dB, strong peaks generated by reflection from the tissue boundary and weak peaks generated by amplitude fluctuations due to backscattering can be distinguished. Using the detected strong peak, the ultrasonic wave transit time between the subcutaneous fat, muscle and bone interface was determined. From this, the thickness of the subcutaneous fat and muscle was obtained by multiplying the speed of sound passing through the tissue (1547 m/s) by half of the ultrasonic wave travel time.

step 3

Subcutaneous fat, muscle, and bone were identified by obtaining the local attenuation coefficients of each tissue. The attenuation coefficient was calculated using a frequency-shift estimator with a window size of 1 μs and 5 independent ultrasound (RF) lines. The diffraction effect of the transducer was corrected with a uniform reference phantom having an attenuation coefficient of 0.511 dB/cm/MHz measured from a tissue-like phantom (PeripheralVascular Doppler FlowPhantom; Model 524; ATS Laboratories, USA).

Verification using MRI

MRI data were obtained for each sample at the same location as the ultrasound measurements using a Bruker Biospec 7T system (BioSpec 70/20 USR; Bruker, Germany).

4A to 4E show a process of processing ultrasound data and corresponding MR images. 4A is a graph showing ultrasonic wave travel time with respect to voltage. 4B illustrates segment averaging and bandpass filtering ultrasound data. 4C shows log-compressed amplitude data, including three prominence peaks of (a) 16.2s, (b) 25.0s, and (c) 66.2s. Figure 4d shows a time versus frequency map including three peaks. As a result of calculation, the thickness of subcutaneous fat and muscle were 6.8 mm and 31.9 mm, respectively. Figure 4e shows an MRI scan of that sample. The yellow line is the ultrasonic measurement direction. It can be confirmed that the thickness of subcutaneous fat and muscle obtained by MRI is similar to the value obtained through processing of ultrasound data.

The attenuation coefficients of subcutaneous fat, muscle, and bone for 16 samples are shown in Table 1 below.

tissue	attenuation coefficient (dB/cm/MHz)
fat	1.8859±0.3599
muscle	1.0982±0.3405
bone	4.5703±0.8298

5A and 5B show a comparison between the tissue thickness obtained from MRI and the thickness obtained through the analysis of ultrasound data. It can be seen that the correlation between the value obtained from the MRI and the value obtained through the analysis of the ultrasound data is very high.

Claims (7)

1. In the method of analyzing ultrasound data obtained by passing through a plurality of layers,

   reducing noise of the ultrasound data;

   identifying an interface between layers by identifying a peak of the ultrasound data after the noise is removed;

   and identifying each layer by identifying an attenuation coefficient of the ultrasound data after the noise is removed.
2. According to claim 1,

   The step of reducing the noise is

   In-band noise cancellation step;

   out-of-band noise cancellation step;

   preserving peak positions in the band pass filtered ultrasound data; and

   converting the ultrasound data into amplitude data.
3. 3. The method of claim 2,

   The peak position preservation is performed by a zero-phase filtering method,

   wherein the amplitude data transformation is performed with a Hilbert transform.
4. According to claim 1,

   The step of identifying the interface between the layers is

   finding a local maximum,

   The local maximum value is obtained using a minimum peak distance (MPD) and a minimum peak prominence (MPP).
5. According to claim 1,

   After understanding the interface between the layers,

   The method further comprising the step of determining the thickness of each layer using the ultrasonic movement speed between the interface.
6. According to claim 1,

   wherein the plurality of layers comprises at least two of bone, muscle and fat.
7. According to claim 1,

   The step of identifying each layer is

   The method further comprising the step of correcting the diffraction characteristics of the transducer using the ultrasound data obtained from the phantom of which the attenuation coefficient is known.

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